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8 papers · 1 filter
Understanding Self-Predictive Learning for Reinforcement Learning
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond +13
We study the learning dynamics of self-predictive learning for reinforcement learning, a family of algorithms that learn representations by minimizing the prediction error of their…
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +6
We consider reinforcement learning in an environment modeled by an episodic, finite, stage-dependent Markov decision process of horizon with states, and actions. The pe…
Power to the People? Opportunities and Challenges for Participatory AI
Abeba Birhane, William Isaac, Vinodkumar Prabhakaran +4
Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum: the increased attention comes partly with the view that participation opens…
AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstruction
Zerui Chen, Yana Hasson, Cordelia Schmid +1
Recent work achieved impressive progress towards joint reconstruction of hands and manipulated objects from monocular color images. Existing methods focus on two alternative repres…
Subverting machines, fluctuating identities: Re-learning human categorization
Christina Lu, Jackie Kay, Kevin R. McKee
Most machine learning systems that interact with humans construct some notion of a person's "identity," yet the default paradigm in AI research envisions identity with essential at…
Marginalized Operators for Off-policy Reinforcement Learning
Yunhao Tang, Mark Rowland, Rémi Munos +1
In this work, we propose marginalized operators, a new class of off-policy evaluation operators for reinforcement learning. Marginalized operators strictly generalize generic multi…